Bibliographic record
Abstract
Abstract Injection or co-injection of solvents has been proposed as an in-situ recovery mechanism for bitumen (Gupta et al., 2002). Solvent-based recovery schemes have several potential advantages over steam-based schemes such as SAGD. These benefits include lower GHG emissions and water requirements, lower capital intensity, and lower operating costs. Additionally, solvent-based recovery schemes have been proposed to improve the quality of produced bitumen (Jossy et al., 2008). The primary mechanism for in-situ upgrading during a solvent injection process is solvent de-asphalting. Solvent de-asphalting is a proven commercial process for processing and upgrading oil in surface facilities such as refineries and upgraders. Several laboratory studies have shown that solvent de-asphalting can also occur in situ, resulting in API improvement in the produced bitumen (AITF report, 2017 and 2018, Brons and Yu, 1995). In this work the economic benefits of upgrading bitumen in-situ will be studied. It will be shown that, at a given price environment, there exists an optimum level of upgrading (as measured by °API). Generally, too little upgrading reduces the value of bitumen due to blending and transportation costs, while too much upgrading reduces bitumen yield and ultimate recovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".